Class Activation Mapping (CAM) Methods:
A common way to harvest localization maps from network activations is by aggregating the information presented in a specific CNN layer. The representative method for aggregating activations is using CAM methods [1]}, which weighs each pixel according to its influence on the class prediction. Several methods have been proposed to improve the mechanism for harvesting the activation maps [2]}, [3]}, [4]}, [5]}. Networks are used for generating CAMs are solely trained for image classification, and thus focus on discriminative image regions. They typically underestimate the object size. To mitigate this issue, [6]}, [7]} employ adversarial perturbation to erase discriminative parts, and for the network to look beyond the discriminative areas. Similarly, in [8]}, [9]} discriminative features are erased, and adversarial learning is adopted to encroach upon non-discriminative regions of the concerned object. In addition, fusion-based methods improve on CAM methods by combining different activation maps based on the classifier's response [10]}, [11]}, [12]}.
